@article {549,
	title = {A systematic review of data quality issues in knowledge discovery tasks},
	journal = {Revista Ingenier{\'\i}as Universidad de Medell{\'\i}n},
	volume = {15},
	year = {2016},
	chapter = {125-150},
	abstract = {<p class="rtejustify"><span style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 12.61px; text-align: start;">Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust.</span></p>
},
	keywords = {amount of data, heterogeneity, incompleteness, inconsistency, noise, outliers, redundancy, timeliness},
	issn = {1692-3324},
	doi = {10.22395/rium.v15n28a7},
	url = {http://www.scielo.org.co/scielo.php?script=sci_arttext\&pid=S1692-33242016000100008},
	author = {Corrales, D. C. and Ledezma, A and Corrales, J. C.}
}
